
import pandas as pd
import plotly.express as plx
from raceplotly.plots import barplot
from tqdm import tqdm
import warnings
from datetime import datetime, timedelta
import plotly.io as pio
import nltk
from nltk.corpus import stopwords
pio.renderers.default = "notebook"
warnings.filterwarnings("ignore")


comp_df = pd.read_csv('complaints.csv/complaints.csv',low_memory=False)
comp_df


comp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5243000 entries, 0 to 5242999
Data columns (total 18 columns):
 #   Column                        Dtype 
---  ------                        ----- 
 0   Date received                 object
 1   Product                       object
 2   Sub-product                   object
 3   Issue                         object
 4   Sub-issue                     object
 5   Consumer complaint narrative  object
 6   Company public response       object
 7   Company                       object
 8   State                         object
 9   ZIP code                      object
 10  Tags                          object
 11  Consumer consent provided?    object
 12  Submitted via                 object
 13  Date sent to company          object
 14  Company response to consumer  object
 15  Timely response?              object
 16  Consumer disputed?            object
 17  Complaint ID                  int64 
dtypes: int64(1), object(17)
memory usage: 720.0+ MB


col_name, total_null= [], []
for x in comp_df.columns:
    col_name.append(x)
    total_null.append(sum(comp_df[x].isnull()))
df_null_info = pd.DataFrame()
df_null_info['column_name'], df_null_info['Total_null'] = col_name, total_null
df_null_info


plx.bar(df_null_info, x = 'column_name', y='Total_null')


col_name, total_unique = [], []
for x in comp_df.columns:
    col_name.append(x)
    total_unique.append(comp_df[x].nunique())
df_unique_count = pd.DataFrame()
df_unique_count['column_name'], df_unique_count['Total_unique'] = col_name, total_unique
df_unique_count


fig = plx.bar(pd.DataFrame(comp_df['Date received'].value_counts()))
fig.update_traces(dict(marker_line_width=0))
fig.update_layout(
        title="No of Complaints in a day over time",
        xaxis_title="Time",
        yaxis_title="Total Complaints",
        font=dict(
            family="Courier New, monospace",
            size=13,
            color="RebeccaPurple"
        )
    )
fig.show()


df_1 = pd.DataFrame(comp_df['Product'].value_counts(dropna=False))
fig = plx.pie(df_1, values=df_1['Product'], names=df_1.index)
fig.update_layout(
        title="Total number of complaints for the desired product",
        font=dict(
            family="Courier New, monospace",
            size=13,
            color="RebeccaPurple"
        )
    )
fig.show()


fig = plx.bar(pd.DataFrame(comp_df['Sub-product'].value_counts() ), orientation='h' )
fig.update_layout(
        title="Total number of complaints for the desired Sub-product",
        height = 700,
        font=dict(
            family="Courier New, monospace",
            size=13,
            color="RebeccaPurple"
        )
    )
fig.show()


fig = plx.bar(pd.DataFrame(comp_df['Issue'].value_counts(dropna=False) ), orientation='h' )
fig.update_layout(
        title="Total number of complaints for the desired Issue",
        height = 750,
        font=dict(
            family="Courier New, monospace",
            size=13,
            color="RebeccaPurple"
        )
    )
fig.show()


fig = plx.bar(pd.DataFrame(comp_df['Sub-issue'].value_counts(dropna=False) ), orientation='h' )
fig.update_layout(
        title="Total number of complaints for the desired Sub-issue",
        font=dict(
            family="Courier New, monospace",
            size=12,
            color="RebeccaPurple"
        )
    )
fig.show()


df_2 = pd.DataFrame(comp_df['Company public response'].value_counts(dropna=False))
fig = plx.pie(df_2, values=df_2['Company public response'], names=df_2.index)
fig.update_layout(
        title="Total number of complaints for the desired \"Company public response\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        font=dict(
            family="Courier New, monospace",
            size=11,
            color="RebeccaPurple"
        )
    )
fig.show()


df_3 = pd.DataFrame(comp_df['Company'].value_counts() )
df_3 = df_3.reset_index()
df_3['Company'], df_3['count'] = df_3['index'], df_3['Company']
df_3.drop(['index'], 1, inplace=True)
df_3['contribution %'] = [((x/len(comp_df)) * 100) for x in df_3['count']]
df_3.head(5)


df_4 = pd.DataFrame(comp_df['State'].value_counts() )
df_4 = df_4.reset_index()
df_4['State'], df_4['count'] = df_4['index'], df_4['State']
df_4.drop(['index'], 1, inplace=True)
df_4


fig = plx.choropleth(df_4, locations='State',locationmode='USA-states', color='count',
                           color_continuous_scale="Viridis",
                           range_color=(0, max(df_4['count'])),
                           scope="north america",
                           labels={'Complaints': 'Complaints Count'}
                          )
fig.update_layout(
    title = "Total number of complaints for the \"State\" in USA " ,
    height = 700,
    margin = dict(t=0, b=0, l=0, r=0),
    font = dict(
        family="Courier New, monospace",
        size=11,
        color="RebeccaPurple"
    ) 
)
fig.show()


df_5 = pd.DataFrame(comp_df['ZIP code'].value_counts() )
df_5 = df_5.reset_index()
df_5['ZIP code'], df_5['count'] = df_5['index'], df_5['ZIP code']
df_5['contribution %'] = [((x/len(comp_df)) * 100) for x in df_5['count']]
df_5.drop(['index'], 1, inplace=True)
df_5


df_5_temp_1 = pd.DataFrame(comp_df)
df_5_temp_1 = df_5_temp_1.where(df_5_temp_1['ZIP code'].str.contains('X'))
df_5_temp_1.dropna(how='all',inplace=True)
df_5_temp_1


df_5_temp_2 = pd.DataFrame(df_5_temp_1['State'].value_counts() )
df_5_temp_2 = df_5_temp_2.reset_index()
df_5_temp_2['State'], df_5_temp_2['hidden_count'] = df_5_temp_2['index'], df_5_temp_2['State']
total_complaints = []
for x in df_5_temp_2['State']:
    df_5_temp_3 = comp_df.where(comp_df['State'] == x)
    df_5_temp_3.dropna(how='all',inplace=True)
    total_complaints.append(len(df_5_temp_3))
df_5_temp_2['Total Complaints'] = total_complaints
df_5_temp_2['Hidden rate %'] = list((df_5_temp_2['hidden_count'] / df_5_temp_2['Total Complaints']) * 100)
df_5_temp_2.drop(['index'], 1, inplace=True)
df_5_temp_2


df_5_temp_2.sort_values('Hidden rate %', inplace=True, ascending=False)
df_5_temp_2.reset_index(inplace=True)
df_5_temp_2


fig = plx.choropleth(df_5_temp_2, locations='State',locationmode='USA-states', color='Hidden rate %',
                           color_continuous_scale="Viridis",
                           range_color=(0, max(df_5_temp_2['Hidden rate %'])),
                           scope="north america",
                           labels={'Complaints': 'Hidden rate'}
                          )
fig.update_layout(
    title = "Total number of Hidden ZIP code for the \"State\" in North America " ,
    height = 700,
    margin = dict(t=0, b=0, l=0, r=0),
    font = dict(
        family="Courier New, monospace",
        size=11,
        color="RebeccaPurple"
    ) 
)
fig.update_traces(text = df_5_temp_2['State'])
fig.show()


df_6 = pd.DataFrame(comp_df['Tags'].value_counts(dropna=False) )
df_6 = df_6.reset_index()
df_6['Tags'], df_6['count'] = df_6['index'], df_6['Tags']
df_6.drop(['index'], 1, inplace=True)
df_6


fig = plx.pie(df_6, values='count', names='Tags')
fig.update_layout(
        title="Total number of complaints for the \"Tags\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        font=dict(
            family="Courier New, monospace",
            size=11,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_6['Tags'], textinfo = 'percent+text')
fig.show()


df_7 = pd.DataFrame(comp_df['Consumer consent provided?'].value_counts(dropna=False) )
df_7 = df_7.reset_index()
df_7['Consumer consent provided?'], df_7['count'] = df_7['index'], df_7['Consumer consent provided?']
df_7.drop(['index'], 1, inplace=True)
df_7


fig = plx.pie(df_7, values='count', names='Consumer consent provided?')
fig.update_layout(
        title="Total number of complaints for the \"Consumer consent provided?\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        font=dict(
            family="Courier New, monospace",
            size=11,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_7['Consumer consent provided?'], textinfo = 'percent+text')
fig.show()


df_8 = pd.DataFrame(comp_df['Submitted via'].value_counts(dropna=False) )
df_8 = df_8.reset_index()
df_8['Submitted via'], df_8['count'] = df_8['index'], df_8['Submitted via']
df_8.drop(['index'], 1, inplace=True)
df_8


fig = plx.pie(df_8, values='count', names='Submitted via')
fig.update_layout(
        title="Total number of complaints for the \"Submitted via\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        height = 800,
        width = 700,
        font=dict(
            family="Courier New, monospace",
            size=15,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_8['Submitted via'], textinfo = 'percent+text')
fig.show()


days_taken_to_process_complaint = []
for i in tqdm(range(0, len(comp_df))):  
    yr_1 = comp_df['Date sent to company'][i].split('-')[0]
    mo_1 = comp_df['Date sent to company'][i].split('-')[1]
    dt_1 = comp_df['Date sent to company'][i].split('-')[2]
    yr_2 = comp_df['Date received'][i].split('-')[0]
    mo_2 = comp_df['Date received'][i].split('-')[1]
    dt_2 = comp_df['Date received'][i].split('-')[2]
    date1 = datetime(int(yr_2), int(mo_2), int(dt_2))
    date2 = datetime(int(yr_1), int(mo_1), int(dt_1))
    difference = date2 - date1
    days_taken_to_process_complaint.append(difference.days)
comp_df['days taken to forward complaint'] = days_taken_to_process_complaint
comp_df

100%|██████████| 5243000/5243000 [02:11<00:00, 40014.30it/s]


df_9 = pd.DataFrame(comp_df['days taken to forward complaint'].value_counts(dropna=False) )
df_9 = df_9.reset_index()
df_9['days taken to forward complaint'], df_9['count'] = df_9['index'], df_9['days taken to forward complaint']
df_9.drop(['index'], 1, inplace=True)
df_9


df_10 = pd.DataFrame(comp_df['Company response to consumer'].value_counts(dropna=False) )
df_10 = df_10.reset_index()
df_10['Company response to consumer'], df_10['count'] = df_10['index'], df_10['Company response to consumer']
df_10.drop(['index'], 1, inplace=True)
df_10


fig = plx.pie(df_10, values='count', names='Company response to consumer')
fig.update_layout(
        title="Total number of complaints for the \"Company response to consumer\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        font=dict(
            family="Courier New, monospace",
            size=15,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_10['Company response to consumer'], textinfo = 'percent+text')
fig.show()


df_11 = pd.DataFrame(comp_df['Timely response?'].value_counts(dropna=False) )
df_11 = df_11.reset_index()
df_11['Timely response?'], df_11['count'] = df_11['index'], df_11['Timely response?']
df_11.drop(['index'], 1, inplace=True)
df_11


fig = plx.pie(df_11, values='count', names='Timely response?')
fig.update_layout(
        title="Total number of complaints for the \"Timely response?\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        height = 450,
        font=dict(
            family="Courier New, monospace",
            size=15,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_11['Timely response?'], textinfo = 'percent+text')
fig.show()


df_12 = pd.DataFrame(comp_df['Consumer disputed?'].value_counts(dropna=False) )
df_12 = df_12.reset_index()
df_12['Consumer disputed?'], df_12['count'] = df_12['index'], df_12['Consumer disputed?']
df_12.drop(['index'], 1, inplace=True)
df_12


fig = plx.pie(df_12, values='count', names='Consumer disputed?')
fig.update_layout(
        title="Total number of complaints for the \"Consumer disputed?\" " ,
        margin=dict(t=0, b=0, l=0, r=0),
        height = 450,
        font=dict(
            family="Courier New, monospace",
            size=15,
            color="RebeccaPurple"
        )
)
fig.update_traces(text = df_12['Consumer disputed?'], textinfo = 'percent+text')
fig.show()


# longest continuous increasing subsequence (subarray).
"""
--> The variable 'i' in the 1st 'for' loop returns the starting index of the required subarray.
--> The variable 'j' in the 2nd 'for' lopp returns the ending index of the required subarray.
--> For example, given array = [1,3,5,4,7]. 
    Possible sub array (1st) = [1,3,5,4,7], 2nd = [3,5,4,7], 3rd = [5,4,7], 4th= [4,7].
--> The approach to find continuous increasing subsequence from the sub array are, 
    For instance we can consider 1st sub array[1,3,5,4,7]. We have to check one by one, like 
    (1,3), (3,5), (5,4), (4,7). We have to break once 1st value is greater than 2nd value in the pair.
    Here the increasing pair count is 2 for this paricular sub array. 
--> The same way we have to check for possible subarray in a list. At each iteration the count is updated. 
--> The longest continuous possible pair is (1,3) and (3,5). The subarray should be [1,3,5], 
    The longth of longest continuous increasing subsequence is possible pair count + 1, which is 2(pairs) + 1 = 3 
--> If the array is in descending order or if an array contains same values ([2,2,2,2,2]). Then, the longest 
    continuous increasing subsequence should be 1, which is [2].
    
"""
arr = [1,3,5,4,7]
#arr = [2,2,2,2,2]
long_incr_subseq = 0
for i in range(0, len(arr)-1):
    count = 0
    new_i = i
    for j in range(i+1, len(arr)):
        curr_val, next_val= arr[new_i], arr[j]
        
        if curr_val < next_val:
            count += 1
        else:
            break
        new_i += 1
    if count > long_incr_subseq:
        long_incr_subseq = count

if long_incr_subseq > 0:
    long_incr_subseq += 1
    print(long_incr_subseq)       
elif long_incr_subseq == 0:
    print(long_incr_subseq + 1)

3


# Largest possible number from the list
"""
--> permutations gives us the possible combinations. 
--> Converting from string to interger, and then maximum of the array gives us the largest possible number.
"""
from itertools import permutations
arr_3 = [3,30,34,5,9]
possible_out = list(permutations(arr_3, len(arr_3)))
possible_out_int = []
for x in possible_out:
    x = ''.join([str(q) for q in list(x)])
    possible_out_int.append(int(x))
print(max(possible_out_int))

9534330


# Store all the "servlet-name", and "servlet-class" in a csv file
"""
--> The json file is saved in the current location of this notebook.
--> 'json' package is used to import and read the required JSON file.
--> "file['web-app']['servlet']" is used to access servlet key of the file(json) which holds all the servlet name and class.
--> The accessed servlet names and classes were stored in the list.
--> 'pandas' library is used to convert the list into dataframe. Then, dataframe is converted into csv file and stored
    in a current location.
--> 
"""
import json
import pandas as pd

with open('DT A1 sample_json (1) (1) (3).json', 'r') as f:
    file = json.load(f)
servlet_name, servlet_class = [], []
for x in file['web-app']['servlet']:
    servlet_name.append(x['servlet-name'])
    servlet_class.append(x['servlet-class'])
df = pd.DataFrame()
df['servlet-name'], df['servlet-class'] = servlet_name, servlet_class
df.to_csv('servlet_name_class.csv',index=False)
df

	Date received	Product	Sub-product	Issue	Sub-issue	Consumer complaint narrative	Company public response	Company	State	ZIP code	Tags	Consumer consent provided?	Submitted via	Date sent to company	Company response to consumer	Timely response?	Consumer disputed?	Complaint ID
0	2024-03-13	Credit card	General-purpose credit card or charge card	Advertising and marketing, including promotion...	Didn't receive advertised or promotional terms	I formally request the removal of an inaccurat...	NaN	EQUIFAX, INC.	MO	631XX	NaN	Consent provided	Web	2024-03-13	Closed with non-monetary relief	Yes	NaN	8538710
1	2024-03-14	Credit reporting or other personal consumer re...	Credit reporting	Incorrect information on your report	Information belongs to someone else	NaN	NaN	EQUIFAX, INC.	PA	19142	NaN	Consent not provided	Web	2024-03-14	Closed with non-monetary relief	Yes	NaN	8551289
2	2023-10-25	Credit reporting or other personal consumer re...	Credit reporting	Improper use of your report	Reporting company used your report improperly	NaN	Company has responded to the consumer and the ...	TRANSUNION INTERMEDIATE HOLDINGS, INC.	CA	92114	NaN	Consent not provided	Web	2023-10-25	Closed with explanation	Yes	NaN	7755832
3	2023-10-25	Mortgage	FHA mortgage	Struggling to pay mortgage	An existing modification, forbearance plan, sh...	NaN	Company has responded to the consumer and the ...	Specialized Loan Servicing Holdings LLC	CA	956XX	NaN	Other	Web	2023-10-25	Closed with explanation	Yes	NaN	7753166
4	2024-05-02	Money transfer, virtual currency, or money ser...	Domestic (US) money transfer	Money was not available when promised	NaN	NaN	NaN	Sigue Corp.	TX	78644	NaN	Other	Web	2024-05-02	In progress	Yes	NaN	8916876
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5242995	2023-08-02	Credit reporting, credit repair services, or o...	Credit reporting	Incorrect information on your report	Information belongs to someone else	NaN	NaN	EQUIFAX, INC.	TX	76036	NaN	Other	Web	2023-08-02	Closed with non-monetary relief	Yes	NaN	7337991
5242996	2023-08-02	Payday loan, title loan, or personal loan	Installment loan	Problem with additional add-on products or ser...	NaN	NaN	NaN	NBT BANCORP INC.	NJ	08046	NaN	NaN	Phone	2023-08-02	Closed with explanation	Yes	NaN	7343156
5242997	2023-08-02	Mortgage	FHA mortgage	Struggling to pay mortgage	NaN	NaN	Company believes it acted appropriately as aut...	Chesmar Homes	TX	77316	NaN	Other	Web	2023-08-17	Closed with explanation	Yes	NaN	7340709
5242998	2023-08-02	Credit reporting, credit repair services, or o...	Credit reporting	Improper use of your report	Reporting company used your report improperly	NaN	NaN	EQUIFAX, INC.	GA	30013	NaN	Consent not provided	Web	2023-08-02	Closed with explanation	Yes	NaN	7341166
5242999	2023-08-04	Payday loan, title loan, or personal loan	Payday loan	Charged fees or interest you didn't expect	NaN	NaN	NaN	Flandreau Santee Sioux Tribe	TX	75057	NaN	Consent not provided	Web	2023-08-18	Closed with explanation	Yes	NaN	7350142

	Company	count	contribution %
0	EQUIFAX, INC.	1094863	20.882377
1	TRANSUNION INTERMEDIATE HOLDINGS, INC.	1016332	19.384551
2	Experian Information Solutions Inc.	925932	17.660347
3	BANK OF AMERICA, NATIONAL ASSOCIATION	140733	2.684208
4	WELLS FARGO & COMPANY	128879	2.458116

	State	count
0	FL	629941
1	CA	597278
2	TX	560364
3	GA	360174
4	NY	336721
...	...	...
58	AA	59
59	MP	57
60	AS	50
61	MH	33
62	PW	13

	ZIP code	count	contribution %
0	XXXXX	120483	2.297978
1	30349	9723	0.185447
2	19143	8231	0.156990
3	33025	7345	0.140092
4	33311	6725	0.128266
...	...	...	...
33652	46200	1	0.000019
33653	87470	1	0.000019
33654	85062	1	0.000019
33655	98141	1	0.000019
33656	98933	1	0.000019

	Date received	Product	Sub-product	Issue	Sub-issue	Consumer complaint narrative	Company public response	Company	State	ZIP code	Tags	Consumer consent provided?	Submitted via	Date sent to company	Company response to consumer	Timely response?	Consumer disputed?	Complaint ID
0	2024-03-13	Credit card	General-purpose credit card or charge card	Advertising and marketing, including promotion...	Didn't receive advertised or promotional terms	I formally request the removal of an inaccurat...	NaN	EQUIFAX, INC.	MO	631XX	NaN	Consent provided	Web	2024-03-13	Closed with non-monetary relief	Yes	NaN	8538710.0
3	2023-10-25	Mortgage	FHA mortgage	Struggling to pay mortgage	An existing modification, forbearance plan, sh...	NaN	Company has responded to the consumer and the ...	Specialized Loan Servicing Holdings LLC	CA	956XX	NaN	Other	Web	2023-10-25	Closed with explanation	Yes	NaN	7753166.0
13	2024-01-19	Credit reporting or other personal consumer re...	Credit reporting	Improper use of your report	Reporting company used your report improperly	15USC 1681 a ( d ) ( 2 ) ( A ) ( i ) clearly s...	Company has responded to the consumer and the ...	Experian Information Solutions Inc.	MD	218XX	NaN	Consent provided	Web	2024-01-19	Closed with explanation	Yes	NaN	8183526.0
15	2024-05-03	Credit reporting or other personal consumer re...	Credit reporting	Problem with a company's investigation into an...	Their investigation did not fix an error on yo...	NaN	NaN	Experian Information Solutions Inc.	NY	117XX	NaN	NaN	Web	2024-05-03	In progress	Yes	NaN	8913449.0
20	2023-09-15	Credit reporting or other personal consumer re...	Credit reporting	Incorrect information on your report	Information belongs to someone else	I am writing to formally dispute and request c...	Company has responded to the consumer and the ...	Experian Information Solutions Inc.	MO	630XX	NaN	Consent provided	Web	2023-09-15	Closed with non-monetary relief	Yes	NaN	7549549.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5242837	2021-10-16	Credit card or prepaid card	General-purpose credit card or charge card	Closing your account	Company closed your account	I sent Capital One written correspondent about...	NaN	CAPITAL ONE FINANCIAL CORPORATION	TX	799XX	NaN	Consent provided	Web	2021-10-16	Closed with explanation	Yes	NaN	4815687.0
5242838	2021-10-16	Money transfer, virtual currency, or money ser...	Mobile or digital wallet	Managing, opening, or closing your mobile wall...	NaN	NaN	NaN	Paypal Holdings, Inc	NaN	XXXXX	NaN	Consent not provided	Web	2021-10-16	Closed with explanation	Yes	NaN	4814403.0
5242840	2021-10-16	Debt collection	I do not know	Written notification about debt	Didn't receive notice of right to dispute	NaN	NaN	The CBE Group, Inc.	MN	563XX	NaN	Other	Web	2021-10-16	Closed with explanation	Yes	NaN	4815034.0
5242844	2021-10-18	Debt collection	Other debt	Attempts to collect debt not owed	Debt was result of identity theft	NaN	NaN	Bread Financial Holdings, Inc.	WV	253XX	NaN	Other	Web	2021-10-18	Closed with explanation	Yes	NaN	4820567.0
5242891	2021-11-20	Mortgage	Conventional home mortgage	Struggling to pay mortgage	NaN	NaN	NaN	21ST MORTGAGE CORP.	AR	716XX	NaN	Other	Web	2021-11-20	Closed with explanation	Yes	NaN	4932173.0

	column_name	Total_unique
0	Date received	4550
1	Product	21
2	Sub-product	86
3	Issue	178
4	Sub-issue	272
5	Consumer complaint narrative	1492267
6	Company public response	11
7	Company	7220
8	State	63
9	ZIP code	33657
10	Tags	3
11	Consumer consent provided?	4
12	Submitted via	7
13	Date sent to company	4499
14	Company response to consumer	8
15	Timely response?	2
16	Consumer disputed?	2
17	Complaint ID	5243000

	State	hidden_count	Total Complaints	Hidden rate %
0	FL	51888	629941	8.236962
1	TX	48719	560364	8.694170
2	CA	37114	597278	6.213857
3	NY	29544	336721	8.774030
4	PA	28872	248582	11.614678
...	...	...	...	...
57	AA	25	59	42.372881
58	MP	19	57	33.333333
59	FM	16	116	13.793103
60	AS	8	50	16.000000
61	MH	3	33	9.090909

	index	State	hidden_count	Total Complaints	Hidden rate %
0	56	GU	163	353	46.175637
1	54	AP	332	749	44.325768
2	51	AE	554	1298	42.681048
3	57	AA	25	59	42.372881
4	53	VI	361	987	36.575481
...	...	...	...	...	...
57	22	AZ	6861	93067	7.372108
58	41	DC	1660	22770	7.290294
59	5	GA	26241	360174	7.285645
60	2	CA	37114	597278	6.213857
61	31	NV	3897	77271	5.043289

	Tags	count
0	NaN	4757083
1	Servicemember	286553
2	Older American	159849
3	Older American, Servicemember	39515

	Consumer consent provided?	count
0	Consent not provided	2099875
1	Consent provided	1838262
2	NaN	1041536
3	Other	254303
4	Consent withdrawn	9024

	Submitted via	count
0	Web	4690912
1	Referral	248657
2	Phone	181242
3	Postal mail	94865
4	Fax	25658
5	Web Referral	1241
6	Email	425

	days taken to forward complaint	count
0	0	4520949
1	1	180347
2	2	85372
3	3	68321
4	4	62247
...	...	...
526	607	1
527	909	1
528	621	1
529	855	1
530	1553	1

	Company response to consumer	count
0	Closed with explanation	3473372
1	Closed with non-monetary relief	1330021
2	In progress	233378
3	Closed with monetary relief	154043
4	Closed without relief	17868
5	Closed	17611
6	Untimely response	11388
7	Closed with relief	5304
8	NaN	15

	servlet-name	servlet-class
0	cofaxCDS	org.cofax.cds.CDSServlet
1	cofaxEmail	org.cofax.cds.EmailServlet
2	cofaxAdmin	org.cofax.cds.AdminServlet
3	fileServlet	org.cofax.cds.FileServlet
4	cofaxTools	org.cofax.cms.CofaxToolsServlet

1)¶

Importing Libraries¶

Importing complaints.csv¶

Looking at the information of DataFrame using info() in pandas package¶

Total null values in the particular column¶

The above dataframe shows columns that don't have a null. The rest of them have a huge null count. Here, mostly, the datatype of the column is string. So first, we're looking at the unique counts of each column.¶

1.1 Visualization of null count of each column in a dataset :¶

Insight :¶

Most of the values in the column 'Tags' are null.

Whereas, 'Date received', 'Product', 'Company', 'Submitted via', 'Date sent to company', 'Timely response?' and 'Complaint ID' has zero null values.

The above Dataframe shows the number of unique values in each column of the dataset.¶

Exploratory Data Analysis¶

1.2 Visualization for Number of complaints in each day(over time) :¶

Insight :¶

The number of complaints increased over time.¶

1.3 Visualization for Number of complaints for desired product : ¶

Insight :¶

"Credit reporting, credit repair services, or other personal consumer reports" contribute 41.3% of total complaints.

Whereas "virtual currency" contributes the least amount of 0.000343% of total complaints.

1.4 Visualization for Number of complaints for desired Sub-product : ¶

Insight :¶

"Credit reporting" category has more than 3 million complaints out of 5 million complaints (total records).

Whereas the "Electronic Benifit Transfer / EBT card" category has the fewest complaints.

1.5 Visualization for Number of complaints for desired Issue : ¶

Insight :¶

The "Incorrect information on your report" category has more than 1.5 million complaints.

Whereas "Lender sold the property" has the least number of complaints.

1.6 Visualization for Number of complaints for desired Sub-issue : ¶

Insight :¶

The "Information belongs to someone else" category has more than 1 million complaints out of 5 million complaints (total records).

Whereas the "Problem with fraud alerts or security freezes" have the least number of complaints.

1.7 Visualization for Number of complaints for desired 'Company public response' : ¶

Insight :¶

52.1% of the dataset is null in 'Company public response' column.

43.2% belong to the category "company has responded to the consumer and the CFPB and chooses not to provide a public response."

Rest of the categories has fewer contribution.

1.8 Visualization for Number of complaints for desired 'Company' : ¶

Insight :¶

The above dataframe shows that the huge amount of complaints(% in double digit) belongs to¶

"EQUIFAX, INC."(20.88%),

"TRANSUNION INTERMEDIATE HOLDINGS, INC. "(19.38%), and

"Experian Information Solutions Inc."(17.66%).

Rest of the companies contributes a few percentage in the total dataset.

1.9 Visualization for Number of complaints for 'State' : ¶

The above map shows total count of complaints for each state in North America.¶

NOTE 1 : Please move the mouse pointer to the shaded area to know the 'State' and 'Total Complaints'.¶

NOTE 2 : The states which has military base(AP, AA, etc.,) are not shown here, due to privacy issue.¶

NOTE 3 : The NAN count is excluded in map chart.¶

Insight :¶

Huge amount of complaints from the State "FL" (Florida), around 0.63 million.

Next to the 'FL', the state "CA" (California) has 0.6 million complaints.

Next to the 'CA', the state "TX" (Texas) has 0.56 million complaints.

1.10 Number of complaints for desired 'ZIP code' : ¶

Insight :¶

There are more than 1.2 lakh zip code of "XXXXX". Company hides that particular ZIP code with 'X' mark.

1.10.1 Visualization for Number of complaints for desired 'ZIP code' which has 'X' mark: ¶

Insight :¶

Around 0.55 million complaints/records contains the 'X' mark in the "ZIP code" column.

The Above map shows the hidden rate of ZIP code for particular state in United States of America.¶

NOTE : The states which has military base(AP, AA, etc.,) are not shown here due to privacy issue.

Insight :¶

The state 'VT', 'ME', 'WV' having huge hidden rate in ZIP code.

Whereas, 'NV', 'CA' states has least amount of complaints which has hidden ZIP code.

1.11 Visualization for Number of complaints for desired 'Tags' : ¶

Insight :¶

There are 3 different tags in the dataset.

90.7% of the tags were null.

"Servicemember" contributes 5.47% of the dataset.

"Older American" contributes 3.05%.

"Older American, Servicemember" holds 0.754%.

1.12 Visualization for Number of complaints for desired 'Consumer consent provided?' : ¶

The above dataframe shows the total number of complaints for each category in the column "Consumer consent provided?".¶

Insight :¶

Around 2 million (40.1%) complaints are consumer consent was not provided.

Around 1.8 million (35.1%) complaints are consumer consent was provided.

Around 1 million (19.9%) complaints are null.

Around 0.25 million (4.85%) complaints were recorded as "others".

The 9024 (0.172%) complaints were recorded in "Consent withdrawn" category.

1.13 Visualization for Number of complaints for desired 'Consumer consent provided?' : ¶

Insight¶